Shubhra (Santu) Karmaker

Computer Science and Software Engineering
Assistant Professor
3106 Shelby Center
334.844.6274
CV
Website

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Education

Ph.D. Computer Science, University of Illinois at Urbana-Champaign
M.S. Computer Science and Engineering, Bangladesh University of Engineering and Technology
B.S. Computer Science and Engineering, Bangladesh University of Engineering and Technology

Research Interests

Big Data
Natural Language Processing
Artificial Intelligence
Information Retrieval
Machine Learning

Keywords/Tags

Big Data Artificial Intelligence Machine Learning Information Retrieval Natural Language Processing

In The News

AI@AU initiative spearheads fall series of expert-led artificial intelligence forums

The AI@AU forums will draw speakers from inside and outside the university to talk on a variety of topical AI and machine learning issues.
Assistant professor in CSSE earns two grants that combine for $1.25 million

Shubhra Kanti (Santu) Karmaker is conducting investigative studies involving natural language processing and artificial intelligence-assisted interactive learning for the National Science Foundation and Air Force Office of Scientific Research.
Artificial intelligence expert in CSSE weighs in on the rise of chatbots

Santu Karmaker discusses quirks, concerns regarding increasingly evolving smart tool
Research team from CSSE, COSAM and agriculture win USDA Coleridge Challenge

After winning the first and second rounds of the Coleridge Initiative Food for Thought Challenge, a team of Auburn faculty members and students from the colleges of engineering, agriculture, and sciences and mathematics, completed the sweep in December.
CSSE team wins 2nd round of Coleridge Initiative Food for Thought Challenge

Assistant professor Santu Karmaker and students Naman Bansal and Alex Knipper advance to the Initiative's third and final round with winners announced November 30
CSSE team uses machine learning to win $10,000 in USDA-sanctioned challenge

ShubhraKanti (Santu) Karmaker, assistant professor in computer science and software engineering, and two doctoral students spent approximately 750 hours in July and August developing a model predicting links between retail scanner data (IRI) and the Food and Nutrient Database for Dietary Studies (FDNSS).